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BIG DATA TECHNIQUES

CODE 106847
ACADEMIC YEAR 2022/2023
CREDITS
  • 6 cfu during the 2nd year of 11267 ECONOMICS AND DATA SCIENCE (LM-56) - GENOVA
  • SCIENTIFIC DISCIPLINARY SECTOR MAT/08
    TEACHING LOCATION
  • GENOVA
  • SEMESTER 2° Semester
    TEACHING MATERIALS AULAWEB

    OVERVIEW

    These lectures provide the students with the comprehension of the main conceptual and computational tools concerned with the interpretation of big amount of data with predictive purposes. Specifically, as far as the data analysis is concerned, the lectures will describe the crucial aspects related to the processing of time series, introduce the Bayesian analysis and filtering, and provide the basics of pattern recognition. The second part of the lectures will be devoted to discuss the main predictive approaches, including regularization theory, machine and deep learning. The teaching approach will combine theoretical aspectes with focus on applications in economics and other applied sciences

     

    AIMS AND CONTENT

    LEARNING OUTCOMES

    The aim of these lectures is to provide students with a fair understanding of the main conceptual and computational tools concerned with the interpretation of big amount of data and with the use of such data for predictive purposes.

    AIMS AND LEARNING OUTCOMES

    The first part of the course involves the crucial aspected related to the processing of time series, the Bayesian analysis and filtering and the basics of pattern recognition. The second part will discuss the main predictive approaches, such as regularization theory, machine and deep learning. The teaching approach will combine the description of the main theoretical aspects of data analysis with some focus on applications in economics and applied sciences

    At the end of the course the students will gain some insights in the computational data analysis world and in the comprehension of some aspects of artificial intelligence. Further, they will obtain skills concerning the computational tools for the processing of data and time series with predictive purposes

     

    PREREQUISITES

    R or Matlab programming

    Data formats and I/O issues

    Basic aspects of numerical analysis and statistics

     

    TEACHING METHODS

    Frontal teaching and computer projects

    SYLLABUS/CONTENT

    Data analysis:

    time series

    Bayesian filtering

    Pattern recognition

    Prediction from data:

    regularization theory

    neural networks

    Bayesian approaches

    deep learning

     

    RECOMMENDED READING/BIBLIOGRAPHY

    Handouts

    TEACHERS AND EXAM BOARD

    Exam Board

    FEDERICO BENVENUTO (President)

    MICHELE PIANA (President)

    LESSONS

    LESSONS START

    To be decided

    Class schedule

    All class schedules are posted on the EasyAcademy portal.